Learning Method and System Using Detached Sensor

ABSTRACT

One embodiment includes a computer-implemented method using a window environment of a display, with a detached sensor, to teach a user. Another embodiment includes a computer system helping teach a user using a detached imaging sensor. In yet another embodiment, a computer-aided-educational system considers a user&#39;s concentration level when teaching the user. The system monitors automatically more than once a user&#39;s concentration-sensitive behavior while the user is working on study materials. Through monitoring the user&#39;s volitional or involuntary behavior, the system provides an indication on the user&#39;s concentration level. Based on the indication, the system could react such as by providing rewards, punishments, and stimulation; or changing the study materials. The system can also react by asking the user a question to stimulate the user or to assess the user&#39;s understanding. Based on the user&#39;s response, the system may change to more appropriate study materials, or different presentation styles.

CROSS-REFERENCE TO RELATED APPLICATIONS

This is a continuation of co-pending U.S. patent application Ser. No. 10/694,706, filed on Oct. 28, 2003, which is a continuation of U.S. Pat. No. 6,699,043, filed on Jan. 14, 2002, which is a continuation of U.S. patent application Ser. No. 09/385,795, filed on Aug. 30, 1999, since abandoned, which is a continuation of U.S. patent Ser. No. 08/689,678, filed on Aug. 13, 1996, now U.S. Pat. No. 5,944,530; all incorporated by reference into this application.

BACKGROUND OF THE INVENTION

The present invention relates generally to learning via a computing device, and more particularly to learning method and system using detached sensor.

Both at home and in schools, the computer is gradually becoming a major medium for education. There are many different reasons for this trend. One is the tremendous reduction in the price of a computer, causing it to permeate into almost every household. Though the price of a computer has been dropping, its computation and memory capacity have increased many folds, leading to computer programs with significantly more intelligence and improved user-friendliness. Another reason is that a computer-aided-education system can be very personalized; it can be tailored to the strengths and weaknesses of individual students. This is very hard to achieve in today's educational environment, in part due to the increase in the students-per-instructor ratio.

One weakness of a computer-aided-educational system is that it typically ignores a student's concentration level. In contrast, a good instructor teaches according to the student's attention span. She can sense whether or not the student is paying attention. Normally, she attributes her sensitivity to her “intuition,” based on years of her teaching experience. One “intuition” is that when the student's pupils start to dilate, the student has lost focus. Another “intuition” is that when the student frowns, he is concentrating. Such “intuition” is very useful in teaching. A good instructor constantly observes such concentration-sensitive behavior, and dynamically adjusts her teaching materials and style accordingly. If most of the students are looking elsewhere instead of at her for a certain period of time, the instructor might stop teaching and tell a joke. This helps the students re-focus back at the instructor. If a student is drooping, the instructor might directly ask her a question to “wake her up.” Such important “intuition” is missing in today's computer-aided-educational systems.

It should be apparent from the foregoing that there is a need for a computer-aided-educational system and method that consider the student's concentration level, while the student is working on the study-materials.

SUMMARY OF THE INVENTION

One embodiment of the invention includes computer-implemented method and system using a window environment of a display, with at least one detached sensor, to teach a user. Another embodiment provides learning methods and systems that help teach a user using at least one detached imaging sensor. In yet another embodiment, the present invention provides a computer-aided-educational system and method that automatically consider a student's concentration-sensitive behavior while the student is working on study materials.

In one embodiment, the present invention includes a presenter, a non-intrusive sensor, a controller and an indicator. The presenter presents study materials on a subject to the student; the non-intrusive sensor automatically monitors more than once the student's concentration-sensitive behavior while the student is working on the materials; the controller analyzes the student's concentration-sensitive behavior based on one or more rules; and the indicator provides an indication on the student's concentration level based on the analysis. In another embodiment, the present invention reacts according to the indication.

There are a number of examples of the concentration-sensitive behavior that the sensor can monitor. In one embodiment, the sensor monitors the student's volitional behavior, such as his inputs into the computer, his facial expressions, his facial orientations and his eyes. In another embodiment, the sensor monitors the student's involuntary behavior, such as the sizes of his pupils.

The controller analyzes one or more of the above behavior based on one or more rules. These rules are similar to the instructor's “intuition.” For example, one rule is as follows: The student has lost concentration in the study materials if for a predetermined period of time, the student's inputs through a mouse have been in a window that does not contain study materials. Another rule is that if the student is not looking at the monitor showing the study materials for a predetermined period of time, the student has lost concentration in the study materials. From the analysis, the system provides an indication on the student's concentration level.

Based on the indication, the system could react accordingly. Different reactions are applicable. Some examples include rewards, punishments, stimulation, and changing the study materials.

In another embodiment, due to the indication, the system asks the student a question, which can stimulate the student and can assess the student's understanding level in the study materials. From the student's response to the question, the system may change to more appropriate study materials and/or presentation style.

The question-asking approach in the above embodiment does not have to be a reaction to the indication. In one embodiment, as the system is presenting study materials to the student, unexpected by the student, the system asks the student a question. After the student responds to the question, the system resumes back to present study materials to the student. In such an embodiment, the question tends to increase the concentration level of the student in the study materials.

In yet another embodiment, the present invention also includes a calibrator, which calibrates the student's concentration-sensitive behavior before the behavior is being monitored to show concentration. One type of calibration establishes the student's behavior when the student is paying attention, and compares it with the student's behavior when the student is working on the study materials. Calibration typically improves the accuracy of the system.

Other aspects and advantages of the present invention will become apparent from the following detailed description, which, when taken in conjunction with the accompanying drawings, illustrates by way of example the principles of the invention.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows one embodiment of the present invention.

FIGS. 2A-B show one embodiment of a system implementing the present invention.

FIG. 3 shows a set of steps to implement one embodiment of the present invention.

FIG. 4 shows examples of volitional behavior monitored by the sensor in the present invention.

FIG. 5 shows another embodiment of the present invention.

Same numerals in FIGS. 1-5 are assigned to similar elements in all the figures. Embodiments of the invention are discussed below with reference to FIGS. 1-5. However, those skilled in the art will readily appreciate that the detailed description given herein with respect to these figures is for explanatory purposes as the invention extends beyond these limited embodiments.

DETAILED DESCRIPTION OF THE INVENTION

FIG. 1 shows one embodiment of a computer-aided-educational system 100 of the present invention. As an overview of some of its components, the system 100 includes a selector 102, which selects study materials from a study-materials storage medium 108 to be presented to a student through a presenter 106. While the student is working on the study materials, a non-intrusive sensor 110 monitors the student's concentration-sensitive behavior, and sends its results back to a controller 104. Then the controller 104 based on one or more rules from a rules storage medium 112 analyzes the monitored results to provide through an indicator 114, an indication on the student's concentration level. In another embodiment, the selector is 102 also connected to the controller 104 to keep track of the study materials presented to the student.

FIG. 2A shows one embodiment of a system 150 implementing the present invention, preferably in software and hardware. The system 150 includes a server computer 152 and a number of client computers, such as 154, which can be a personal computer. Each client computer communicates to the server computer 152 through a dedicated communication link, or a computer network 156.

FIG. 2B shows one embodiment of a client computer 154. It typically includes a bus 159 connecting a number of components, such as a processing unit 160, a main memory 162, an I/O controller 164, a peripheral controller 166, a graphics adapter 168 and a network interface adapter 170. The I/O controller 164 is connected to components, such as a harddisk drive 172 and a floppy disk drive 174. The peripheral controller 166 is connected to peripheral components, such as a keyboard 176, a mouse 182, and a digital camera 180. The graphics adapter 168 is connected to a monitor 178; and the network interface adapter 170 is connected to the network 120. The network can be the Internet, an intranet, the world wide web and other forms of networks.

Different components of the present invention can be in different elements shown in FIGS. 2A-B. For example, the presenter 106 and the sensor 110 can be in a client computer; the selector 102, the controller 104, the study-materials storage medium 108, the rules storage medium 112 and the indicator 114 can be in the server computer 152. In another embodiment, the selector 102, the controller 104 and the indicator 114 are also in a client computer. Different components can be in different elements in the above description. Nonetheless, there is no restriction preventing all components to reside in one element, such as a client computer. A number of operations in the present invention can be implemented by software, which is controlled, for example, by the processing unit 160. In yet another embodiment, the number of operations implemented by software can be stored in a storage-medium, which can be, for example, the main memory 162 or a CD read-only-memory.

The present invention is applicable to teach any subject or materials that can be taught by a computer. The teaching period may last one semester or a year, or just one class session. The materials may cover inter-disciplinary areas, such as electrical engineering and thermodynamics, or computer networking and programming techniques. The materials may just be for training a field engineer on a new product. In the following, mathematics is the subject used to illustrate the present invention.

In one embodiment, the subject is divided into major-topics, with each major-topic subdivided into minor-topics, and with each minor-topic further subdivided into line-items. Each line-item typically covers one well-defined area in the subject. In another embodiment, the subject is further divided into more levels below the line-items; and in a third embodiment, the subject is just divided into line-items.

As an example of line-items, if the major-topic is high school algebra, then it can be divided into the following line-items, with bracketed terms served as comments:

High School Algebra (the major-topic)

(Minor-topics under the major-topic)

Decimal Numbers

Polynomials

Linear Equations

Quadratic Equations

. . .

Integers

(Line-items under the minor-topic of integers)

Addition & Subtraction (Difficulty level 1)

Multiplication (Difficulty level 2)

Division (Difficulty level 2)

Prime Numbers (Difficulty level 3)

Factorization (Difficulty level 3)

Common Divisor (Difficulty level 4)

. . .

Fractions

(Line-items under the minor-topic of fractions)

Addition & Subtraction (+/−) with Common Denominator

(Difficulty level 3)

+/− with Integers (Difficulty level 4)

+/− without Common Denominator (Difficulty level 5)

Multiplication and Divisions (*,/) with Integers (Difficulty level 5)

*,/ with fraction (Difficulty level 6)

Compound Fractions (Difficulty level 6)

Fraction Reduction (Difficulty level 7)

Ratios and Proportions (Difficulty level 7)

. . .

Another example with the minor topic being differential calculus is as follows:

Calculus (major topic)

Differential calculus (minor topic)

Fractions (Difficulty level 1)

Polynomials (Difficulty level 1)

Exponential Functions (Difficulty level 1)

Differentiation (Difficulty level 2)

Differentiate a sum (Difficulty level 3)

Differentiate a product (Difficulty level 3)

Differentiate a quotient (Difficulty level 4)

In one embodiment, each line-item has a difficulty level. The bracketed difficulty level next to each line-item in the above example indicates how difficult one line-item is relative to other line-items in the subject, or how significant one is relative to another. A line-item with a low difficulty level is a relatively easy line-item or a relatively less important line-item. Typically, a student learning a subject starts from learning line-items at the lowest difficulty level.

The lists of items in the above examples are generated based on expert knowledge on the subject of mathematics. With the proper instruction, such as through reading the present specification, generating such lists with the difficulty levels should be obvious to experts in the subject. The more knowledgeable the expert, the more complete the sets of items.

In one embodiment, each line-item is represented by a line-item root, which includes the line item and its root. In the above example, the root of a line-item includes its subject, its major topic and minor topic.

In one embodiment, the selector 102 starts the learning process by selecting a line-item with the lowest difficulty level. If there are a number of those, one of them is randomly selected. Study materials for that line-item are retrieved from the study-materials storage medium 108 to be presented to the student. After presentation, the selector 102 selects another line-item with the lowest difficulty level among all the un-selected line-items, and the process repeats. For this embodiment, each line-item also includes a mode attribute, which is changed from the un-selected to the selected mode after the study materials for that line-item has been selected to be worked on by the student.

To select a set of study materials from the study-material storage medium 108, the selector 102 sends the line-item root to the storage medium 108 to retrieve the corresponding study materials. Typically, there are a number of sets of study materials in the storage-medium 108, and they can be in the following format:

(line-item root, mode, study materials)

The following serves as examples of study materials for differentiating polynomial:

First, the system teaches the approach to generate derivatives based on the basic principle in differentiation, such as:

df(x)/dx=lim _(h→0)((f(x+h)−f(x))/h)

Then the system teaches the generalized equation, such as:

((dΣa _(i) x ^(i))/dx)=(Σi*a _(i) x ^(i-1))

Finally, the system teaches the importance of and the way to find optima and minima by solving the following equation:

((dΣa _(i) x ^(i))/dx)=0

Based on the line-item root, and with one set of study materials per line-item, the selector 102 retrieves from the study-materials storage-medium 108, the corresponding set of study materials. Creating study materials on a subject should be obvious to experts in the subject, and will not be further discussed in this application.

The selector 102 then sends the retrieved study materials to the presenter 106. The study materials can be a document with no questions, arranged as a list of screens. The presenter 106 typically includes the monitor 178, which presents the study materials to the student, who can go from one screen to another with the keyboard 176, or the mouse 182. In another embodiment, the study materials are broadcast through a radio. As the student is working on the study materials presented through the radio, the student's concentration-sensitive behavior is monitored automatically.

In another embodiment, the study materials only have questions. Typically, students gain a better understanding on a subject through actively working on questions than through passively reading study materials. In one embodiment, each question is embedded in a question entry, which is of the following format:

(line-item root, mode, question-body, answer).

The term “question-body” describes the body of a question. The following serves as an example:

Subject: Mathematics.

Major-topic: High School Algebra.

Minor-topic: Fraction.

Line-item: +/− with common denominator

Mode: Un-selected

Answer Question-body

28/37 What is the sum of 2/37, 3/37, 8/37 and 15/37?

−2/43 17/43−25/43+6/43=?

The selector 102 sends to the study-materials storage medium 108 the line-item root to retrieve the set of questions with the same line-item root.

An example of study materials with questions are for the line-item of differentiating exponential functions. A number of questions are generated, including the question on expanding an exponential function based on Taylor expansion, the question on differentiating the Taylor-expanded exponential function, whose answer is the original Taylor-expanded exponential function, and the question on differentiating the exponential function, whose answer is the exponential function.

In another embodiment, the study materials include study materials with questions and study materials without questions.

Note that the formats of the study materials may change as the student progresses. The student can learn one line-item based on questions, and another based on study materials with no questions. As an example, for differential calculus, of the different line-items, all of them can be learnt through either study materials with or without questions, except for the line-item of differentiation, which is typically learnt without questions. That study-materials cover the general differentiation concept, such as the following:

df(x)/dx=lim _(h→0)((f(x+h)−f(x))/h)

FIG. 3 shows a set of steps 250 to implement one embodiment of the present invention. First, the presenter 106 presents (step 252) the selected study materials to the student. As the student is working on the study materials, the sensor 110 monitors (step 254) more than once the student's concentration-sensitive behavior, and feeds those monitored results to the controller. The controller 104 analyzes (step 256) the results based on one or more rules to provide (step 258) an indication on the student's concentration. Based on the indication, the system reacts (step 260) accordingly.

A type of concentration-sensitive behavior is a type of behavior that is sensitive to one's concentration. As one's concentration changes, such a type of behavior changes accordingly. The behavior can be physical, psychological, biological, emotional and physiological.

In the step of monitoring (step 254), the sensor automatically monitors more than once the student's concentration-sensitive behavior while the student is working on the study materials. Instead of just monitoring once to determine concentration level, monitoring more than once increases the accuracy in determining the student's concentration level. For example, a student is concentrating on the study materials. A mosquito lands on the back of his right hand. As the student is trying to hit the mosquito, the system monitors him. The indication based on that image alone is a correct indication of the student's concentration level in the study materials at that specific instant. However, the single measurement is not a good indication of the student's actual concentration level in the study materials—that single measurement is an outlying point that should be deleted. Instead of just one single result, this embodiment monitors more than once the student's behavior, which enhances identifying a pattern to eliminate outlying points.

The monitoring step does not have to stop after monitoring twice. The monitoring step can continue in a periodic manner, such as once every two seconds. In the embodiments of monitoring more than once or monitoring periodically, the results can be analyzed to identify patterns.

The behavior monitored more than once can be of the same type, or can be of different types. In one embodiment, in monitoring more than once, the sensor monitors the same type of behavior each time. In another embodiment, in monitoring more than once, the sensor monitors more than one type of behavior; for example, a first monitoring process is on one type of behavior, and a second monitoring process is on another type of behavior. In monitoring more than one type of behavior, the sensor may include more than one type of sensor, which can monitor more than one type of behavior substantially simultaneously. Monitoring more than one type of behavior is similar to monitoring one type of behavior more than once, in the sense that both approaches increase the accuracy in determining the student's concentration level.

FIG. 4 shows examples of different types of concentration-sensitive behavior, which are volitional 300. In one embodiment, the sensor 110 monitors the student's volitional inputs entered into the computer (box 302). One type of volitional inputs is entered through the keyboard 176 or the mouse 182. The study materials can be presented to the student through the monitor 178. As the student works on the study materials, he enters commands through the keyboard 176 or a position-pointing device, such as the mouse 182, or arrow buttons of the keyboard 176. The inputs may be the downward or upward arrows on the keyboard or the mouse, or may be the typing speed through the keyboard.

In one embodiment, the sensor 110 monitors the speed of inputs by the student as a function of time. There are different ways to monitor the speed of inputs, such as polling periodically the corresponding devices of those inputs. Such monitoring process should be obvious to those skilled in the art, and will not be further described.

As the student starts working on the study materials, the inputs are entered at a certain speed. As the student gets tired, or as the student loses concentration, this speed typically decreases. In this embodiment, the student's input speed is compared with a reference speed to identify changes.

There are a number of methods to determine the reference speed. In one embodiment, this reference speed is set through randomly sampling many students. Based on the students' responses on similar study materials, a reference speed is determined. In another embodiment, the student's initial speed becomes the reference speed. This initial speed may be found for example by averaging the student's speed across five minutes, such as from the first one minute to the first six minutes of the student's usage.

Different types of study materials typically have different reference speeds. For example, if the study materials include no pictures, the input speed may be slow because the student has to read an entire screen of text. If the student has to compose a sentence, the speed is likely to have frequent short pauses because the student has to think to compose the sentence. To accommodate such variations, in one embodiment, the reference speed is a function of the difficulty level of the study materials. As the student progresses in working on the study materials, the difficulty level of the study materials typically increases. In one embodiment, the reference speed is divided by the following factor:

(The difficulty level of the study material*a constant).

With the above equation, as the difficulty level increases, the reference speed decreases accordingly. In this embodiment, the reference speed tracks the difficulty level of the study materials.

As discussed above, by monitoring the speed of the student's inputs, the system 100 can provide an indication of the student's concentration. Thus, one rule is as follows:

If the speed of the student's volitional inputs across a predetermined period of time is significantly lower than the reference speed, the student has lost concentration in the study materials.

In one embodiment, the predetermined period of time is two minutes; and more than three times slower is considered as significantly lower.

In another embodiment, the study materials are presented through a multi-windows environment. The student enters inputs into the system, such as through a position-pointing device, like the mouse 182, or through the keyboard 176. In one embodiment, the sensor 110 in this embodiment is implemented through software, which periodically, such as every two seconds, polls the operating system or the device drivers of the position-pointing device. The polling determines if there have been any inputs. Writing such software to monitor such inputs to the system should be obvious to those skilled in the art, and will not be further described in this application. In such an embodiment, one rule is as follows:

If for a predetermined period of time, the inputs have been entered outside the window where the study materials reside, the student has lost concentration in the study materials.

In yet another embodiment, the study materials are presented in a window environment that has a focus window, and the sensor 110 can sense the focus window, for example as in the above embodiment. In such an embodiment, one rule is as follows:

If the study materials are not in the focus window for a predetermined period of time, the student has lost concentration in the study materials.

In one embodiment, the predetermined amount of time is more than one minute. If monitoring is performed every three seconds, in one minute, the system would have performed 20 measurements.

In yet another embodiment, the sensor 110 senses another type of volitional behavior, which is based on the student's face. In this embodiment, the monitor 178 presents study materials. The sensor 110 including the digital camera 180 are positioned adjacent to the monitor 178, as shown, for example in FIG. 2A. With the camera positioned where it is, when the student is looking at the monitor to work on the study materials, the digital camera 180 could take digital images of the student's face. Taking the digital images to generate numerous bits of data should be obvious to those skilled in the art and will not be further described in this application.

To improve the performance of this embodiment, before the step of monitoring, the present invention includes the step of calibration through imaging. One calibration technique enters the student's image before the student works on the study materials, and uses that image as the reference to compare with other images. For example, before the student starts working on the study materials, he is asked to look at the monitor 178 with a message box having a message such as “LOOK AT ME,” and with a picture of two eyes staring at the student. Then, the digital camera 180 takes a reference image of the student's face, who typically looks at the two eyes.

The reference image should be analyzed. That image includes not only the student's face, but also background information, such as the wall of a room. In one embodiment, the controller assumes that the student's two eyes are looking at the two eyes in the monitor. Based on this assumption, the student's face is determined. Such image recognition techniques are disclosed for example in “Computer Recognition of Human Faces,” written by Takeo Kanada, and published by Birkhauser Verlag, Basel and Stuttgart, in 1977. Even if the distance between the monitor and the student's face increases, the relative distances among different features on his face remain the same. In one embodiment, the monitoring step focuses on relative distances to re-calibrate the student's face. Such image recognition techniques should be obvious to those skilled in the art, and will not be further described in the present application.

One type of facial information is the facial orientation (box 304). The controller 104 connected to the digital camera 180 calibrates the facial orientation when the student is looking at the monitor 178. This reference image could be just the oblong shape of the face. After calibration, when the student starts working on the study materials, the digital camera 180 regularly captures the facial image, such as once every few seconds. All information in that image is removed leaving behind the orientation of the face. These orientations are compared with the reference image to check for differences. The distance between the monitor and the student's face may change. To compensate for such changes, in one embodiment, the controller uses the ratio of the longest horizontal to the longest vertical distance of the oblong shape. If the captured facial orientation is significantly different from the reference facial orientation, the student is not looking at the monitor. The student may be looking away from the computer or drooping while falling asleep. In such an embodiment, one rule is as follows:

If the student's facial orientation is significantly different from its reference image as shown in two consecutive monitoring processes, the student has lost concentration in the study materials.

In one embodiment, two images are considered significantly different if their horizontal-to-vertical-distance ratios differ by more than 20%.

Another type of facial information is the condition of the eyes (box 306). If the eyelids are covering significant portions of the irises, the student's eyes are closing. In such an embodiment, one rule is as follows:

If the eyelids cover more than 60% of the irises as shown in two consecutive monitoring processes, the student has lost concentration in the study materials.

Another type of facial information is the student's facial expressions (box 308), such as whether the student is frowning or not. In such an embodiment, one rule is as follows:

If the student frowns in two consecutive monitoring processes, the student is concentrating on the study materials.

Concentration-sensitive behavior can be involuntary. In one embodiment, the sensor 110 monitors the sizes of the student's pupils, assuming that a student's pupil dilates if the student loses focus and concentration. In such an embodiment, one rule is as follows:

If the average size of the student's pupils dilates by more than 20% as compared to the average size of the reference image for a predetermined amount of time, the student has lost concentration in the study materials.

Other examples of involuntary concentration-sensitive behavior include the student's heart beat, breathing rate, body temperature and whether the student's sweat has increased. With appropriate sensors and rules, these involuntary behavior can be monitored to provide indications on whether the student has lost concentration in the study materials.

There are many other types of concentration-sensitive behavior. Different types of behavior coupled with their corresponding sensors and rules should be able to indicate the student's concentration level.

More than one type of the student's concentration-sensitive behavior can be monitored by one or more sensors. In fact, one type can be volitional, with the other type involuntary. Including different types of behavior tends to increase the accuracy of identifying the student's concentration level. With more than one type of behavior being monitored, the system may not have to monitor each type of behavior more than once to identify the student's concentration level. An example of a rule for such an embodiment is as follows:

If the student's facial orientation is different from the reference image by more than 20% while the student's eyelids are covering more than 60% of the irises, the student has lost concentration in the study materials.

Such rules should be obvious to experts in the field of human perception, and will not be further described in this application.

The above embodiments describe whether the student has lost concentration or not. However, the invention is also applicable to indicate the student's degree of concentration, such as ranging from low, medium to high. For example, if the student has not lost concentration in the study materials for a long period of time, the student's concentration level is high. Another example is that if the student's eyes are wide open with his inputs through the mouse moving down the study materials in a fairly constant speed for a long duration of time, such as five minutes, the student's concentration level is also high.

In another embodiment, if the controller 104 decides that the student has not lost concentration for a long period of time, such as ten minutes, the controller 104 averages the captured results during that time frame—with outlying points removed—and treats the averaged results as the reference, which will be used to compare with subsequent captured results, to determine if the student has lost concentration in the study materials. The reference can be a reference image, such as the student's face, or the student's input speed, as appropriately modified by the study materials' difficulty level, or other monitored results. As the student continues working on study materials, this reference can be updated regularly by averaging it with subsequent captured results, which also show that the student has not lost concentration. Unlike many of the previously described references, which are static, this type of reference is typically not a constant, and is known as a dynamic reference. It is usually more closely tailored to the student. With more data used to generate the dynamic reference, its accuracy is typically better than the static references.

In yet another embodiment, the system 100 asks for the student's identity, such as the student's name, when the student starts working on the study materials. After the student enters his identity, it is stored in the system 100. The student's reference information, whether static or dynamic, is stored with the student's identity in the memory of the system 100, such as its harddisk. After the first working session, if the student wants to work on study materials through the system 100 again, the system retrieves from its memory the student's reference information. For such an embodiment, the retrieved reference information can replace the step of calibration. If the reference is of the dynamic type, the retrieved information is regularly updated.

Based on one or more of the above concentration-sensitive behavior coupled to one or more appropriate rules, the indicator 114 provides an indication on the student's concentration (step 258), or the student's degree of concentration. Such indication can be as simple as changing the state of a register—a high logic level indicates the student has not lost concentration, while a low logic level indicates the student has. Another indication can be printing a report indicating that the student's degree of concentration in the study materials for a period of time.

In one embodiment, the system reacts according to the indication (step 260). Some examples of reactions include stimulation, rewards, punishments or changing the study materials.

If the indication is that the student has lost concentration in the study materials, one way the controller 104 can help the student to re-focus on the study materials is through stimulation. This includes presenting a real-life application of the study materials that the student has lost concentration in. The stimulation can be through sound. It can be visual effects, including changing the screen temporarily and then restoring to the previous screen.

Another type of stimulation includes allowing the student to play a game. This stimulation is applicable if the student has been working on the study materials for a long duration of time, and should have a break. Thus, after the student has worked for a long period of time, such as 45 minutes, and is losing concentration in the study materials, the controller can pose the student a question, such as, “Do you want to take a break and play a game?” If the student wants to, in one embodiment, the controller accesses a game from the study-materials storage medium, which includes a number of games. The game serves as a diversion. Not only does it distract the student's mind for some time, the game also relaxes and entertains the student. After the game, presentation is resumed on the study materials.

Another form of reaction is a reward. If the student has been concentrating for a long period of time, at the end of a section in the study materials, the system reacts by praising the student audibly through a speaker, or visually through the monitor with words like “TIME FOR A SNACK!” Other examples of rewards include playing a short piece of music, presenting a joke, a factoid on an interesting subject, or playing a short animation or video clip.

A further form of reaction is punishment. This includes generating a report indicating that the student has lost concentration for a long period of time so that the student's supervisor can punish the student accordingly. Another punishment may be an audible reprimand, such as “PAY ATTENTION!”

The system can also change the study materials according to the monitored results. If the student has lost concentration in working on the study materials for a predetermined amount of time, the system can react by changing the study materials to a different set of materials. Also, the presenter 106 may change the presentation style accordingly, such as by reducing the speed of presentation through increasing the line spacing of the text or the size of the image to present to the student.

In yet another embodiment, due to the indication, the system asks the student a question. This question can stimulate the student, and help the student to re-focus in the study materials. Typically, the question is based on the study materials just presented to the student.

As a side note, while the controller 104 through the sensor 110 monitors the student's concentration-sensitive behavior, the controller 104 can also track the corresponding study materials being presented to the student. Such an embodiment has the added benefit of tying the indication with the corresponding study materials presented to the student.

Back to the embodiment that asks the student a question, this embodiment can be achieved, for example, through the selector 102 sending to the study-materials storage medium 108 the line-item root of the study materials just presented. From the line-item root, a set of questions with the same line-item root is retrieved, and one of those question is randomly selected for the student. Other ways may be used to generate a question on materials just presented to the student. One simple way is to randomly select a sentence that has just been presented to the student, and change the syntax of that sentence into a question.

The embodiment on asking questions has a number of benefits. Even if the student does not know the answer to the question, typically, the student is stimulated by the question. Also, the question can be used to assess the student's understanding level on the materials just presented to the student. After the student answers the question, if the answer is correct, the controller 104 can praise the student appropriately. If the answer is not correct, the student may not understand what has just been presented. The controller 104 has a number of options. For example, the study materials just presented can be presented to the student again; the location as to where he can find the answer to the question can be presented to the student; the location of the answer can be hyperlinked to the location of the wrong answer if the student activates an icon shown on the presenter 106; the presenter 106 presents study materials that are easier than the one just presented to the student, such as one with a lower difficulty level; or the presenter 106 can resume presenting, and ignore the wrong answer altogether.

The above embodiment on asking questions can be modified to focus on increasing the student's concentration level. FIG. 5 shows such an embodiment 350. The system presents (step 352) study materials to the student. Then, the system asks the student a question unexpectedly (step 354). As an example, if the study material is presented through the monitor, unexpectedly, the entire screen changes. From a screen of study materials, the system suddenly changes the screen to display a question. The unexpected nature of the change, together with the displaying of the question stimulate the student. To further enhance the effect of stimulation, the system can spell out the question while displaying it. Typically the question is based on the study materials the student has been working on. After the student responds to the question, the system resumes (step 356) presenting the study materials to the student.

The question stimulates. Right before the question is presented, the student may be concentrating or may not be concentrating. Either way, the student, unlikely to be aware that a question is coming, is suddenly confronted with a question. Independent of whether the student knows the answer, the question typically increases the student's concentration level. Also, responding to a question is an active learning approach, as compared to the passive learning approach of reading. The more active learning approach together with the unexpected nature of the question tend to increase the student's memory retention in the subject matter covered by the question.

Another benefit provided by the question is that the student's answer to the question provides an indication on the student's understanding level in the study materials. As described above, if the answer is wrong, the system can go over that part of the study materials, or can reduce the difficulty levels of the study materials to be presented to the student. In another embodiment, the question is just for increasing concentration; the system ignores the answer, and continues on with the presentation.

The student might have stopped working on the study materials altogether. For the embodiment that monitors the student's inputs, if there is no inputs for a predetermined amount of time, such as ten minutes, the system assumes that the student has totally stopped working on the study materials.

The present invention teaches sensing through different types of non-intrusive sensors, which are defined as sensors that do not cause the student physical pain and suffering when they are sensing the student's concentration-sensitive behavior. As technology progresses, sensors that are intrusive today can become non-intrusive in the future, for example, sensors that monitor the student's brain waves, which can be a type of concentration-sensitive behavior.

Rules are stored in the rules storage medium. However, in one embodiment, the rules have previously been embedded in the software implementing the present invention. With rules already embedded in the software, there is no need for accessing the rules, and there is no need for the system to have the rules storage medium.

In the above embodiments, the student's behavior is monitored more than once before the step of analysis. In another embodiment, the monitoring step and the analysis step are intermixed. Instead of monitoring more than once and then analyzing the results, in this embodiment, the sensor monitors one type of behavior, with the result analyzed. Then the sensor monitors the same or a different type of behavior, with the result analyzed.

In yet another embodiment, the steps in the present invention repeat. For example, after the step of reacting according to the indication (step 260) or providing an indication (step 258), the invention repeats from the step of monitoring automatically (step 254). In this embodiment, study materials are continually presented to the student, although the study materials might be changed due to the reaction (step 260).

The rules discussed can be self-adapting. In other words, the controller 104 can change a rule after applying the rule to a number of situations and after analyzing the results. This can be done, for example, in a fussy-logic system.

Other embodiments of the invention will be apparent to those skilled in the art from a consideration of this specification or practice of the invention disclosed herein. It is intended that the specification and examples be considered as exemplary only, with the true scope and spirit of the invention being indicated by the following claims. 

1. A computer-implemented method for teaching materials using a first window of a display, with at least some of the space outside of the first window being viewable via the display, with the space outside of the first window including a second window at least partially viewable via the display, the method comprising: acquiring data, by a computing device, regarding a volitional input of a user, the data being sensed by a sensor monitoring a physical attribute associated with the user, the sensor being detached from the user; and analyzing the data by the computing device, which is coupled to the sensor and the display; and determining by the computing device from the analyzing of the data whether the volitional input is in the first window or in the second window, whereby the volitional input being in the second window indicates a learning situation different from the volitional input being in the first window.
 2. A computer-implemented method for teaching a user as recited in claim 1 further comprising adjusting, by the computing device, materials for presenting to the user, wherein the adjusting comprises switching to alternative materials that are different from materials that would have been presented if the determining determines that the volitional input is in the first window, instead of the second window.
 3. A computer-implemented method for teaching a user as recited in claim 1 wherein the volitional input includes a behavior based on a facial expression of the user.
 4. A computer-implemented method for teaching a user as recited in claim 3 wherein the facial expression includes the user frowning.
 5. A computer-implemented method for teaching a user as recited in claim 1 wherein the method further comprises using a rule regarding speed of input to analyze a volitional input of the user to help teach the user.
 6. A computer-implemented method for teaching a user as recited in claim 1 wherein the method further comprises determining understanding of the user in materials presented to the user.
 7. A computer-implemented method for teaching a user as recited in claim 1 further comprising, in view of the analyzing, having materials retrieved to be presented to the user to help teach the user.
 8. A computer-implemented method for teaching a user as recited in claim 1 further comprising being aware of whether or not the user is focusing in materials presented to the user.
 9. A computer-implemented method for teaching a user as recited in claim 1 further comprising asking the user a question in view of the analyzing to help teach the user.
 10. A computer-implemented method for teaching a user as recited in claim 1, wherein the sensor includes a camera, and wherein the data includes an image of the user.
 11. A computer-implemented method as recited in claim 10 further comprising analyzing more than one image of the user sensed by the camera to determine whether the volitional input is in the first window or in the second window.
 12. A computer-implemented method as recited in claim 10 further comprising asking the user to be in front of the camera for acquiring the data.
 13. A computer-implemented method as recited in claim 10 further comprising: acquiring, in a learning session, another piece of data regarding a volitional input of the user, the another piece of data being sensed by the sensor monitoring the physical attribute associated with the user, the sensor being detached from the user; and comparing the another piece of data with the data to help teach the user materials during the learning session.
 14. A computer-implemented method as recited in claim 1, wherein the sensor includes an imaging sensor configured to acquire data regarding volitional inputs of the user at different times, and wherein the method further comprises having materials retrieved to teach the user based upon changes in the volitional inputs of the user sensed by the sensor monitoring the physical attribute associated with the user.
 15. A computer-implemented method as recited in claim 1 further comprising: linking the data to an identity of the user; acquiring, in a learning session, another piece of data regarding a volitional input of the user, the data being sensed by the sensor monitoring the physical attribute associated with the user, the sensor being detached from the user; and comparing the another piece of data with the data to help teach the user materials during the learning session.
 16. A computer system for teaching comprising: an imaging sensor configured to sense data regarding volitional behaviors of a user at different times; computer memory having materials to teach the user; and a controller associated with the computer memory, the controller configured to communicate with the sensor and analyze the data to have materials retrieved to teach the user based upon changes in the volitional behaviors, wherein the sensor is configured to be detached from the user when the sensor is sensing data from the user.
 17. A computer system as recited in claim 16, wherein the sensor includes a camera, and wherein the data includes an image of the user.
 18. A computer system as recited in claim 16, wherein the volitional behaviors include behaviors based on a facial expression of the user.
 19. A computer-implemented method to help teach materials comprising: capturing in a learning session, a first piece of information regarding a volitional input of a user, by a sensor monitoring a physical attribute associated with the user, the sensor being detached from the user; linking by a computing device, which is coupled to the sensor, the first piece of information to an identity of the user; storing the first piece of information with the identity of the user in a memory device; capturing in another learning session, a second piece of information regarding a volitional input of the user, by the sensor monitoring the physical attribute associated with the user, the sensor being detached from the user; retrieving, by the computing device, the first piece of information from the memory device; and comparing, by the computing device, the second piece of information with the first piece of information to help teach the user materials.
 20. A computer-implemented method as recited in claim 19 further comprising adjusting materials for presenting to the user in view of the comparing, wherein the adjusting comprises switching to alternative materials that are different from materials that would have been presented. 